Image processing apparatus, display device, and image processing method

ABSTRACT

According to one embodiment, an image processing apparatus includes an obtaining module, a coefficient filter, a large area filter, and a determination module. The obtaining module obtains a feature value indicating an image structure of a pixel block comprising a target pixel to be processed and a surrounding pixel neighboring the target pixel in an input image. The coefficient filter performs filtering on the pixel block using a filter coefficient corresponding to the feature value. The large area filter performs smoothing on a pixel block that are larger than blocks used with coefficient filter. The determination module determines to cause the large area filter to perform the smoothing when the image structure indicated by the feature value is smooth.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2009-217807, filed Sep. 18, 2009, theentire contents of which are incorporated herein by reference.

BACKGROUND

1. Field

One embodiment of the invention relates to an image processingapparatus, a display device, and an image processing method.

2. Description of the Related Art

Filtering has been performed with an edge-preserving smoothing filter asimage processing to remove noise, such as mosquito noise and blocknoise, from an image. The filtering uses a filter coefficient selectedbased on a detection result obtained from each pixel block formed of apixel to be processed and pixels surrounding it. If an edge is presentat the boundary of pixel blocks or near the boundary, an appropriatefilter coefficient may not be selected, and the edge may be smoothed. Inview of this, for example, Japanese Patent Application Publication(KOKAI) No. 2005-79617 discloses a conventional technology in which afilter is selected from a plurality of edge-preserving smoothingfilters. More specifically, distortion at the boundary of pixel blocksand the magnitude of the distortion are detected. A pixel range to befiltered is specified based on the detection result, and one of theedge-preserving smoothing filters is selected.

With the edge-preserving smoothing filter, a portion of a pixel blockhaving a smooth image structure may not be sufficiently smoothed. Insuch a case, it is efficient to perform filtering on a larger pixelblock. With the conventional technology, a pixel range is specifiedbased on the presence of distortion at the boundary of pixel blocks andthe magnitude of the distortion, and, even if the image structure of apixel block is smooth, the pixel block is not changed so that filteringis performed on a larger pixel block. Accordingly, efficient filteringis not performed.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

A general architecture that implements the various features of theinvention will now be described with reference to the drawings. Thedrawings and the associated descriptions are provided to illustrateembodiments of the invention and not to limit the scope of theinvention.

FIG. 1 is an exemplary functional block diagram of an image processingapparatus according to an embodiment of the invention;

FIG. 2 is an exemplary functional block diagram of a histogram generatorin the embodiment;

FIG. 3 is an exemplary schematic diagram for explaining the calculationof an orientation histogram in the embodiment;

FIG. 4 is an exemplary schematic diagram for explaining the conversionof the orientation histogram in the embodiment;

FIG. 5 is an exemplary flowchart of the operation of the histogramgenerator in the embodiment;

FIG. 6 is an exemplary schematic diagram for explaining filtering usinga filter coefficient from the converted orientation histogram in theembodiment;

FIG. 7 is an exemplary functional block diagram of a filtering module inthe embodiment;

FIG. 8 is an exemplary schematic diagram for explaining the relationshipbetween an absolute difference from a center pixel and the weight of asurrounding pixel in a multi ε filter (binary) in the embodiment;

FIG. 9 is an exemplary schematic diagram for explaining the relationshipbetween an absolute difference from a center pixel and the weight of asurrounding pixel in a simple ε filter and a bilateral filter in theembodiment;

FIG. 10 is an exemplary flowchart of the operation of the multi ε filterin the embodiment;

FIG. 11 is an exemplary schematic diagram for explaining therelationship between an absolute difference from a center pixel and theweight of a surrounding pixel in a multi ε filter (three-value) in theembodiment;

FIG. 12 is an exemplary schematic diagram of a third pixel block inlarge area filtering in the embodiment;

FIG. 13 is an exemplary flowchart of the operation of the filteringmodule related to the large area filtering in the embodiment;

FIG. 14 is an exemplary functional block diagram of a hardwareconfiguration of a computer in the embodiment;

FIG. 15 is an exemplary functional block diagram of a hardwareconfiguration of a computer in the embodiment; and

FIG. 16 is an exemplary functional block diagram of a filtering moduleaccording to a modification of the embodiment.

DETAILED DESCRIPTION

Various embodiments according to the invention will be describedhereinafter with reference to the accompanying drawings. In general,according to one embodiment of the invention, an image processingapparatus comprises an obtaining module, a coefficient filter, a largearea filter, and a determination module. The obtaining module isconfigured to obtain a feature value indicating an image structure of apixel block comprising a target pixel to be processed and a surroundingpixel neighboring the target pixel in an input image. The coefficientfilter is configured to perform filtering on the pixel block using afilter coefficient corresponding to the feature value. The large areafilter is configured to perform smoothing on a pixel block that arelarger than blocks used with coefficient filter. The determinationmodule is configured to determine to cause the large area filter toperform the smoothing when the image structure indicated by the featurevalue is smooth.

According to another embodiment of the invention, a display devicecomprises an obtaining module, a coefficient filter, a large areafilter, a determination module, and a display module. The obtainingmodule is configured to obtain a feature value indicating an imagestructure of a pixel block comprising a target pixel to be processed anda surrounding pixel neighboring the target pixel in an image. Thecoefficient filter is configured to perform filtering on the pixel blockusing a filter coefficient corresponding to the feature value. The largearea filter is configured to perform smoothing on a pixel block that arelarger than blocks used with coefficient filter. The determinationmodule is configured to determine to cause the large area filter toperform the smoothing when the image structure indicated by the featurevalue is smooth. The display module is configured to display the imagefiltered by the coefficient filter or the image smoothed by the largearea filter according to determination of the determination module.

According to still another embodiment of the invention, there isprovided an image processing method comprising: an obtaining moduleobtaining a feature value indicating an image structure of a pixel blockcomprising a target pixel to be processed and a surrounding pixelneighboring the target pixel in an input image; a coefficient filterperforming filtering on the pixel block using a filter coefficientcorresponding to the feature value; a large area filter performingsmoothing on a pixel block that are larger than blocks used withcoefficient filter; and a determination module determining to cause thelarge area filter to perform the smoothing when the image structureindicated by the feature value is smooth.

FIG. 1 is a functional block diagram of an image processing apparatus 1according to an embodiment of the invention.

As illustrated in FIG. 1, the image processing apparatus 1 comprises ahistogram generator 10, a template table storage module 20, an edgeparameter calculator 30, and a filtering module 40. The image processingapparatus 1 performs noise removal filtering on an input image such as avideo and a still image by the filtering module 40, and outputs theimage from which noise has been removed.

In the noise removal of the embodiment, a separate histogram for eachdirection (hereinafter, “orientation histogram”) related to a pixelgradient (a differential value of pixels) in a pixel block is used as afeature value of the pixel block. As illustrated in FIG. 1, thehistogram generator 10 generates an orientation histogram of a pixelblock of a predetermined size including a pixel to be filtered andsurrounding pixels neighboring the pixel in an input image. That is, thehistogram generator 10 obtains a feature value indicating the imagestructure of the pixel block.

The template table storage module 20 stores in advance a template table(a lookup table) that stores filter coefficients corresponding tovarious forms of the orientation histogram. More specifically, thetemplate table storage module 20 stores filter coefficients eachcorresponding to an orientation histogram as a feature value indicatingthe image structure of a pixel block in units of pixel blocks of apredetermined size (first pixel block, which will be described later)formed of a plurality of pixels. The template table storage module 20refers to the template table and outputs a filter coefficientcorresponding to an orientation histogram generated for each pixel blockby the histogram generator 10 to the filtering module 40.

The filter coefficient stored in the template table storage module 20may be generated, for example, by obtaining a regression curve (curvedsurface model) fitted to a plurality of pixels (kernel) with a pixel tobe filtered as a center pixel (H. Takeda, S. Farsiu, and P. Milanfar“Kernel Regression for Image Processing and Reconstruction”, IEEE Trans.Image Proc., Vol. 16, No. 2, pp. 349-366, 2007). By generating a filtercoefficient equivalent to the regression curve fitting and associatingthe filter coefficient with an orientation histogram, it is possible toimplement filtering achieving both sharpness and noise removal.

The edge parameter calculator 30 calculates an edge parameter indicatingthe direction, intensity, and sharpness of an edge contained in eachpixel block. The filtering module 40 performs filtering on an inputimage and outputs the image from which noise has been removed. Morespecifically, based on a filter coefficient from the template tablestorage module 20 and an edge parameter calculated by the edge parametercalculator 30, the filtering module 40 performs convolution operationusing the filter coefficient with respect to each pixel block.

The histogram generator 10 will now be described in detail. FIG. 2 is afunctional block diagram of the histogram generator 10. As illustratedin FIG. 2, the histogram generator 10 comprises an orientation histogramcalculator 11 and an orientation histogram converter 12.

The orientation histogram calculator 11 calculates an orientationhistogram of a pixel block (second pixel block) larger than a pixelblock (first pixel block) to be filtered using a filter coefficient. Theorientation histogram converter 12 converts (normalizes) the orientationhistogram calculated for the second pixel block to an orientationhistogram corresponding to the first pixel block.

FIG. 3 illustrates an example of the calculation of an orientationhistogram. FIG. 4 illustrates an example of the conversion of theorientation histogram. As illustrated in FIG. 3, the orientationhistogram calculator 11 calculates a direction component and anintensity component from vertical and horizontal differential values ofpixels contained in a pixel block. Then, based on the directioncomponent and the intensity component, the orientation histogramcalculator 11 calculates an orientation histogram for, for example, eachof four directions 0 to 3. In this manner, the orientation histogramcalculator 11 calculates the orientation histogram of a second pixelblock.

As illustrated in FIG. 4, a second pixel block B2 is larger than a firstpixel block B1 to be filtered using a filter coefficient. Therefore,image characteristics can be identified in a wider range in the secondpixel block B2 than in the first pixel block B1. However, since thefilter coefficient corresponds to the orientation histogram of the firstpixel block B1, the orientation histogram of the second pixel block B2cannot be used as it is and needs to be converted to an orientationhistogram corresponding to the first pixel block B1. The orientationhistogram converter 12 converts the orientation histogram of the secondpixel block B2 to an orientation histogram corresponding to the firstpixel block B1 based on the ratio of areas between the first pixel blockB1 and the second pixel block B2. For example, if the first pixel blockB1 is formed of 3×3 pixels, and the second pixel block B2 is formed of7×7 pixels, the orientation histogram converter 12 multiplies theorientation histogram of the second pixel block B2 by 9/49 to convert itto an orientation histogram corresponding to the first pixel block B1.

FIG. 5 is a flowchart of the operation of the histogram generator 10. Asillustrated in FIG. 5, the orientation histogram calculator 11calculates an orientation histogram of the second pixel block (S11).Then, the orientation histogram converter 12 converts the orientationhistogram calculated by the orientation histogram calculator 11 to anorientation histogram corresponding to the size of the first pixel block(S12).

Thereafter, the histogram generator 10 determines whether an orientationhistogram is calculated for all pixel blocks of pixels of an input image(S13). When an orientation histogram has been calculated for all thepixel blocks (Yes at S13), the process ends. On the other hand, when anorientation histogram has not yet been calculated for all the pixelblocks (No at S13), the process returns to S11 to calculate anorientation histogram of a pixel block for which an orientationhistogram has not yet been calculated.

FIG. 6 illustrates an example of filtering using a filter coefficientfrom a converted orientation histogram. As illustrated in FIG. 6, thehistogram generator 10 calculates an orientation histogram (a featurevalue) of the second pixel block B2 larger than the first pixel block B1to be filtered using a filter coefficient. The histogram generator 10converts (normalizes) the orientation histogram of the second pixelblock B2 to an orientation histogram corresponding to the first pixelblock B1 based on the ratio of areas between the first pixel block B1and the second pixel block B2 and the like.

Generally, as the pixel block to be filtered using a filter coefficientis smaller, the template table stored in the template table storagemodule 20 can be smaller and the convolution operation can be reduced atthe time of filtering. However, as the pixel block is smaller, onlylocal characteristics can be identified in an input image. For example,if an edge boundary overlaps the boundary between pixel blocks, the edgeshape cannot be accurately determined in a small pixel block and anappropriate filter coefficient cannot be selected. As a result, when apixel block is small, the quality of an output image after filteringdegrades.

The histogram generator 10 identifies characteristics of an input imagein the second pixel block B2 larger than the first pixel block B1.Accordingly, the histogram generator 10 is capable of generating anorientation histogram indicating the characteristics accurately andthereby increasing the filter performance. Besides, the histogramgenerator 10 converts an orientation histogram calculated for the secondpixel block B2 into an orientation histogram corresponding to the firstpixel block B1. This prevents an increase in the required memorycapacity of the template table storage module 20 and the operationamount at the time of filtering.

If the first pixel block B1 has n pixels both vertically andhorizontally from the center pixel and is in a size of (2n+1)×(2n+1)pixels, the second pixel block B2 may have 2n pixels both vertically andhorizontally from the center pixel and be in a size of (4n+1)×(4 n+1)pixels (n: a natural number not including 0). When the first pixel blockB1 and the second pixel block B2 satisfy such a relationship, byincreasing the value of n, the ratio of areas between the first pixelblock B1 and the second pixel block B2 can be approximated to 1:4. Thisfacilitates the normalization operation.

Further, if the first pixel block B1 has n pixels both vertically andhorizontally from the center pixel and is in a size of (2n+1)×(2 n+1)pixels, the second pixel block B2 may be in a size of 8 m×8 m pixels (n,m: both natural numbers satisfying the relationship 2 n+1<8 m). When thefirst pixel block B1 and the second pixel block B2 satisfy such arelationship, a central processing unit (CPU), a digital signalprocessor (DSP), and the like that handle data in multiples of 8 bitscan efficiently calculate an orientation histogram.

The filtering module 40 will be described in detail below. FIG. 7 is afunctional block diagram of the filtering module 40. As illustrated inFIG. 7, the filtering module 40 comprises a multi ε filter 41, acoefficient filter 42, switches 43 and 44, a large area ε filter 45, anda large area filter 46.

The multi ε filter 41 performs ε filtering using multiple thresholds ε(hereinafter, “multi ε filtering”) with respect to each pixel block(first pixel block) to be filtered of a pixel in an input image. In theε filtering, the multi ε filter 41 compares an absolute differencebetween a pixel to be processed (center pixel) and each surroundingpixel in a pixel block with the threshold ε, and replaces the pixelvalue of a surrounding pixel with an absolute difference exceeding thethreshold ε by that of the center pixel. By the multi ε filtering usingmultiple thresholds ε, the multi ε filter 41 performs such replacementof pixel values in stages.

FIG. 8 illustrates an example of the relationship between an absolutedifference from the center pixel and the weight of a surrounding pixelin a multi ε filter (binary). As illustrated in FIG. 8, when the multi εfiltering is performed with, for example, two thresholds ε0 and ε1(ε0<ε1), the replacement of pixel values is performed in three stageswith the pixel value of the center pixel, the average of pixel values ofthe center pixel and a surrounding pixel, and the pixel value of thesurrounding pixel. More specifically, a surrounding pixel with aabsolute difference exceeding the threshold ε1 is weighted by 0, and itspixel value is replaced by that of the center pixel. A surrounding pixelwith a absolute difference less than the threshold ε1 and exceeding thethreshold ε0 is weighted by 0.5, and its pixel value is replaced by theaverage of pixel values of the center pixel and the surrounding pixel. Asurrounding pixel with a absolute difference less than the threshold ε0is weighted by 1, and its pixel value is left original without beingreplaced.

FIG. 9 illustrates an example of the relationship between an absolutedifference from the center pixel and the weight of a surrounding pixelin a simple ε filter and a bilateral filter. As illustrated in FIG. 9,with a simple ε filter having one threshold, pixel value variation afterfiltering may not be smooth at a portion where pixel values vary largelysuch as at an edge. A bilateral filter assigns a higher weight to thepixel value of a surrounding pixel with a smaller absolute differencefrom that of the center pixel to be processed and convolutes the pixelvalue. With the bilateral filter, pixel value variation is smooth afterfiltering. However, the bilateral filter is not suitable for high-speedoperation such as single instruction multiple data (SIMD) operation.

On the other hand, the multi ε filter 41 of the embodiment mixes thepixel value of the center pixel with the pixel values of surroundingpixels in stages using a plurality of thresholds ε. With this, even at aportion where pixel values vary largely such as at an edge, pixel valuevariation after filtering can be smooth. In addition, the replacementusing the thresholds ε and the calculation of the average can beperformed at a high speed by the SIMD operation or the like.

FIG. 10 is a flowchart of the operation of the multi ε filter 41. Morespecifically, FIG. 10 illustrates the multi ε filtering using the twothresholds ε0 and ε1 (ε0<ε1). As illustrated in FIG. 10, first, themulti ε filter 41 determines whether an absolute difference between thecenter pixel and a surrounding pixel exceeds the threshold ε1 in a pixelblock corresponding to each pixel in an input image (S21). If theabsolute difference exceeds the threshold ε1 (Yes at S21), the multi εfilter 41 replaces the pixel value of the surrounding pixel with that ofthe center pixel. Then, the process moves to S25.

On the other hand, if the absolute difference does not exceed thethreshold ε1 (No at S21), the multi ε filter 41 determines whether theabsolute difference between the center pixel and the surrounding pixelexceeds the threshold ε0 (S23). If the absolute difference exceeds thethreshold ε0 (Yes at S23), the multi ε filter 41 replaces the pixelvalue of the surrounding pixel with the average of the pixel values ofthe center pixel and the surrounding pixel (S24). Then, the processmoves to S25.

On the other hand, if the absolute difference does not exceed thethreshold ε0 (No at S23), the multi ε filter 41 leaves the pixel valueof the surrounding pixel as original, and the process moves to S25.

The multi ε filter 41 determines whether the multi ε filtering has beenperformed for all pixel blocks corresponding to all pixels of the inputimage (S25). If the multi ε filtering has been performed for all thepixel blocks corresponding to all the pixels (Yes at S25), the processends. If not (No at S25), the process returns to S21 to perform themulti ε filtering on a pixel block of a pixel on which the multi εfiltering is yet to be performed.

FIG. 11 illustrates an example of the relationship between an absolutedifference from the center pixel and the weight of a surrounding pixelin a multi ε filter (three-value). As illustrated in FIG. 11, the multiε filter may use not only two thresholds 6 but also three or morethresholds ε. In the case of three thresholds, the rate at which asurrounding pixel is weighted is set in stages such that the ratio ofthe pixel value of the center pixel increases as a absolute differenceincreases. More specifically, with respect to the thresholdsε0:ε1ε2=1:2:3, the rate at which surrounding pixels are weighted is setin stages such that the weights of the surrounding pixels are at a rateof 3:2:1 according to the rate of each of the thresholds. This enablespixel value variation after filtering to be smoother.

As illustrated in FIG. 7, the coefficient filter 42 performs convolutionoperation with a filter coefficient output from the template tablestorage module 20 with respect to each pixel block (first pixel block)on which the multi ε filtering has been performed by the multi ε filter41, thereby filtering each pixel. The filtering module 40 removes noisefrom an input image with this filtering. Since the multi ε filtering isperformed by the multi ε filter 41 before the coefficient filter 42performs the filtering, the filtering by the coefficient filter 42 isnot affected by a surrounding pixel with a large difference from thecenter pixel in the pixel block. Thus, appropriate filtering can beperformed.

The switches 43 and 44 switch a processing route for each pixel in animage filtered by the coefficient filter 42 based on the edge parameterindicating the direction, intensity, and sharpness of an edge calculatedby the edge parameter calculator 30. More specifically, if the value ofthe edge parameter (for example, intensity, sharpness, or the like of anedge) is sufficiently small and smaller than a preset value and a pixelblock to be filtered has a smooth image structure, the switches 43 and44 switch the processing route so that the pixel block is processed bythe large area ε filter 45 and the large area filter 46. On the otherhand, if the value of the edge parameter exceeds the preset value and apixel block to be filtered has an image structure of an edge or acorner, the switches 43 and 44 switch the processing route so that thepixel block is output as pixels of an output image without beingprocessed by the large area ε filter 45 and the large area filter 46.

The large area ε filter 45 performs ε filtering on each pixel of a thirdpixel block (large area pixel block) larger than the first pixel blockas a processing range (hereinafter, “large area ε filtering”). Morespecifically, in the large area ε filtering, with a pixel to beprocessed as a center pixel G1, a third pixel block B3 larger than thefirst pixel block B1 is set as a processing range as illustrated in FIG.12. The large area ε filter 45 compares an absolute difference betweenthe center pixel G1 and each surrounding pixel in the third pixel blockB3 with the threshold ε, and replaces the pixel value of a surroundingpixel with an absolute difference exceeding the threshold ε by that ofthe center pixel.

The large area ε filtering may be the multi ε filtering. However, sincethe large area ε filter 45 performs the filtering on a pixel having asmooth image structure, the large area ε filtering is preferably simpleε filtering using one threshold ε for simplification of the processing.In view of this, the large area ε filtering of the embodiment may besimple ε filtering using one threshold ε.

The large area filter 46 performs smoothing (large area filtering) usinga mean filter on each pixel block (third pixel block) on which the largearea ε filter 45 has performed the large area ε filtering. Morespecifically, the large area filter 46 averages pixel values of pixelsin a pixel block.

With only the multi ε filter 41 and the coefficient filter 42 thatremove noise from the first pixel block B1, an area having a smoothimage structure may not be sufficiently smoothed. If the size of thefirst pixel block B1 is increased to handle this, it is necessary toincrease the size of the template table stored in the template tablestorage module 20. This causes an increase in the required memorycapacity of the template table storage module 20 and the operationamount of the coefficient filter 42 at the time of filtering.

Accordingly, in the embodiment, the switches 43 and 44 switch theprocessing route so that the large area ε filter 45 and the large areafilter 46 process an area having a smooth image structure. With this,the large area filtering is performed on the third pixel block B3 largerthan the first pixel block B1. Thus, more efficient filtering can beperformed on an area having a smooth image structure, and image qualityafter the filtering can be increased. Besides, while the large areafiltering is performed on pixels more than those of the first pixelblock B1, the processing can be performed with the same or lessoperation amount than the amount of operation (multiplication related toconvolution operation) of the coefficient filter 42 that increases inthe case of filtering on the first pixel block B1 having an increasedsize.

If the first pixel block B1 has n pixels both vertically andhorizontally from the center pixel and is in a size of (2n+1)×(2 n+1)pixels, the third pixel block B3 may have 2n pixels both vertically andhorizontally from the center pixel and be in a size of (4n+1)×(4 n+1)pixels (n: a natural number not including 0).

Further, if the first pixel block B1 has n pixels both vertically andhorizontally from the center pixel and is in a size of (2n+1)×(2 n+1)pixels, the third pixel block B3 may be in a size of 8 m×8 m pixels (n,m: both natural numbers satisfying the relationship 2 n+1<8 m). When thefirst pixel block B1 and the third pixel block B3 satisfy such arelationship, a CPU, a DSP, and the like that handle data in multiplesof 8 bits can efficiently perform the large area filtering on the thirdpixel block B3.

FIG. 13 is a flowchart of the operation of the filtering module 40related to the large area filtering. As illustrated in FIG. 13, whenprocessing starts on a predetermined pixel in an image filtered by thecoefficient filter 42, the switches 43 and 44 determine whether an edgeparameter indicating the intensity, sharpness, or the like of an edgecalculated by the edge parameter calculator 30 is sufficiently small andsmaller than a preset value (S31).

When the intensity, sharpness, or the like of the edge is sufficientlysmall (Yes at S31), the large area ε filter 45 performs the large area εfiltering on the predetermined pixel (S32), and the large area filter 46performs the large area filtering on the predetermined pixel (S33).Then, the process moves to S34. On the other hand, when the intensity,sharpness, or the like of the edge is not sufficiently small (No atS31), the process moves to S34 without the large area ε filtering andthe large area filtering on the predetermined pixel.

The filtering module 40 determines whether the processing has beenperformed for all pixels in the image filtered by the coefficient filter42 (S34). If the processing has been performed for all the pixels (Yesat S34), the process ends. If not, the process returns to S31 to performthe processing on a predetermined pixel on which the processing is yetto be performed.

FIG. 14 is a functional block diagram of a hardware configuration of acomputer 100. As illustrated in FIG. 14, the computer 100 comprises aCPU 101, an operation module 102, a display module 103, a read onlymemory (ROM) 104, a random access memory (RAM) 105, a signal inputmodule 106, and a storage module 107, which are connected by a bus 108.

The CPU 101 uses a predetermined area in the RAM 105 as a work area, andexecutes various computer programs stored in advance in the ROM 104 orthe like to implement various types of processing. The CPU 101 controlsthe overall operation of the computer 100. Further, the CPU 101 executesa computer program stored in advance in the ROM. 104 or the like toimplement the image processing method of the embodiment.

The operation module 102 comprises various types of input keys. Theoperation module 102 receives information input by the user as an inputsignal and outputs it to the CPU 101.

The display module 103 comprises a display panel such as a liquidcrystal display (LCD) panel or the like to display various types ofinformation, an output image processed as described above, and the likebased on a display signal from the CPU 101. The display module 103 maybe integrated with the operation module 102 to form a touch panel.

The ROM 104 is a nonvolatile memory that stores various types of settinginformation, computer programs related to the control of the computer100, and the like. The RAM 105 is a storage medium such as a synchronousdynamic random access memory (SDRAM). The RAM 105 provides the work areato the CPU 101 and functions as a buffer.

The signal input module 106 is an interface that receives electricalsignals of a still image, video, sound, and the like, and outputs thesignals to the CPU 101. The signal input module 106 may be acommunication interface to a broadcast program receiver (tuner), theInternet, and the like.

The storage module 107 comprises a magnetic or optical recording medium.Under the control of the CPU 101, the storage module 107 stores datasuch as a still image, video, sound, and the like received through thesignal input module 106.

FIG. 15 illustrates a hardware configuration of a computer 100 a. Asillustrated in FIG. 15, the computer 100 a may be, for example, an imageprocessing DSP, and comprises an image processor 101 a that implementsthe image processing method of the embodiment. As described above, theimage processing method of the embodiment may be implemented by thecooperation of the CPU 101 and a computer program (hereinafter, “imageprocessing program”), and may also be implemented by a dedicated imageprocessing DSP or the like. The computer 100 (100 a) may be aninformation processor such as a personal computer (PC), a display devicesuch as a television receiver, a recorder for recording a broadcastprogram received by a tuner, or the like.

The image processing program executed by the CPU 101 may be provided tothe computer as being stored in the ROM or the like. The imageprocessing program may also be provided to the computer as being storedin a computer-readable storage medium, such as a compact disc-read onlymemory (CD-ROM), a flexible disk (FD), a compact disc recordable (CD-R),and a digital versatile disc (DVD), as a file in an installable orexecutable format.

The image processing program may also be stored in a computer connectedvia a network such as the Internet so that it can be downloadedtherefrom via the network. Further, the display processing program mayalso be provide or distributed via a network such as the Internet.

A description will be given of a modification of the filtering module40. FIG. 16 is a functional block diagram of a filtering module 40 aaccording to the modification. As illustrated in FIG. 16, the filteringmodule 40 a is basically similar in configuration to the filteringmodule 40 except for the presence of switches 43 a and 44 a in place ofthe switches 43 and 44.

The switches 43 a and 44 a switch a processing route for each pixel inan input image based on an edge parameter indicating the direction,intensity, and sharpness of an edge calculated by the edge parametercalculator 30. More specifically, if the value of the edge parameter(for example, intensity, sharpness, or the like of an edge) issufficiently small and smaller than a preset value and a pixel block tobe filtered has a smooth image structure, the switches 43 a and 44 aswitch the processing route so that the pixel block is processed by thelarge area ε filter 45 and the large area filter 46. On the other hand,if the value of the edge parameter exceeds the preset value and a pixelblock to be filtered has an image structure of an edge or a corner, theswitches 43 a and 44 a switch the processing route so that the pixelblock is processed by the multi ε filter 41 and the coefficient filter42.

In an area of an input image having a smooth image structure, noiseremoval effect may be small and the large area filtering may suffice fornoise removal. In such a case, the filtering module 40 a switches theprocessing route to perform efficient filtering without performing lessefficient filtering. Moreover, the switching of the processing routeenables an appropriate ε filter to be selected. More specifically, thelarge area ε filtering using one threshold ε suitable for efficientfiltering is selected for an area having a smooth image structure, whilethe multi ε filtering suitable for the coefficient filter 42 to performappropriate filtering is selected for an area having an image structureof an edge or a corner.

The various modules of the systems described herein can be implementedas software applications, hardware and/or software modules, orcomponents on one or more computers, such as servers. While the variousmodules are illustrated separately, they may share some or all of thesame underlying logic or code.

While certain embodiments of the inventions have been described, theseembodiments have been presented by way of example only, and are notintended to limit the scope of the inventions. Indeed, the novel methodsand systems described herein may be embodied in a variety of otherforms; furthermore, various omissions, substitutions and changes in theform of the methods and systems described herein may be made withoutdeparting from the spirit of the inventions. The accompanying claims andtheir equivalents are intended to cover such forms or modifications aswould fall within the scope and spirit of the inventions.

1. An image processing apparatus comprising: an obtaining moduleconfigured to obtain an orientation histogram related to a pixelgradient in a pixel block indicating an image structure of the pixelblock comprising a target pixel to be processed and a surrounding pixelneighboring the target pixel in an input image; a coefficient filterconfigured to perform filtering equivalent to fitting the pixel block toa regression curve using a filter coefficient corresponding to theregression curve fitting the pixel block indicated by the orientationhistogram; a large area filter configured to perform smoothing on alarge area pixel block that are larger than the pixel block with thetarget pixel at a center; and a determination module configured todetermine to cause the large area filter to perform the smoothing whenthe image structure indicated by the orientation histogram is smooth. 2.The image processing apparatus of claim 1, further comprising a largearea ε filter configured to compare a difference between a pixel valueof the target pixel and a pixel value of the surrounding pixel with apredetermined threshold and, when the difference exceeds thepredetermined threshold, perform replacement of the pixel value of thesurrounding pixel in the large area pixel block before the large areafilter performs the smoothing.
 3. The image processing apparatus ofclaim 1, wherein the large area filter is configured to perform thesmoothing with a mean filter to average pixel values of pixels in thelarge area pixel block.
 4. The image processing apparatus of claim 1,wherein the pixel block comprises (2n+1)×(2 n+1) pixels, the large areapixel block comprises (4n+1)×(4 n+1) pixels, and n is a natural numbernot including
 0. 5. The image processing apparatus of claim 1, whereinthe pixel block comprises (2n+1)×(2 n+1) pixels, the large area pixelblock comprises 8 m×8 m pixels, and n and m are natural numberssatisfying relationship 2 n+1<8 m.
 6. (canceled)
 7. A display devicecomprising: an obtaining module configured to obtain an orientationhistogram related to a pixel gradient in a pixel block indicating animage structure of the pixel block comprising a target pixel to beprocessed and a surrounding pixel neighboring the target pixel in animage; a coefficient filter configured to perform filtering equivalentto fitting the pixel block to a regression curve using a filtercoefficient corresponding to the regression curve fitting the pixelblock indicated by the orientation histogram; a large area filterconfigured to perform smoothing on a large area pixel block that arelarger than the pixel block with the target pixel at a center; adetermination module configured to determine to cause the large areafilter to perform the smoothing when the image structure indicated bythe orientation histogram is smooth; and a display module configured todisplay the image filtered by the coefficient filter or the imagesmoothed by the large area filter according to determination of thedetermination module.
 8. An information processing method comprising: anobtaining module obtaining an orientation histogram related to a pixelgradient in a pixel block indicating an image structure of the pixelblock comprising a target pixel to be processed and a surrounding pixelneighboring the target pixel in an input image; a coefficient filterperforming filtering equivalent to fitting the pixel block to aregression curve using a filter coefficient corresponding to theregression curve fitting the pixel block indicated by the orientationhistogram; a large area filter performing smoothing on a large areapixel block that are larger than the pixel block with the target pixelat a center; and a determination module determining to cause the largearea filter to perform the smoothing when the image structure indicatedby the orientation histogram is smooth.